Python OpenCV实现鼠标画框效果


						使用Python+OpenCV实现鼠标画框的代码，供大家参考，具体内容如下







# -*-coding: utf-8 -*-

"""

 @Project: IntelligentManufacture

 @File : user_interaction.py

 @Author : panjq

 @E-mail : pan_jinquan@163.com

 @Date : 2019-02-21 15:03:18

"""

# -*- coding: utf-8 -*-

 

import cv2

from utils import image_processing

import numpy as np

global img

global point1, point2

global g_rect

 

def on_mouse(event, x, y, flags, param):

 global img, point1, point2,g_rect

 img2 = img.copy()

 if event == cv2.EVENT_LBUTTONDOWN: # 左键点击,则在原图打点

  print("1-EVENT_LBUTTONDOWN")

  point1 = (x, y)

  cv2.circle(img2, point1, 10, (0, 255, 0), 5)

  cv2.imshow('image', img2)

 

 elif event == cv2.EVENT_MOUSEMOVE and (flags & cv2.EVENT_FLAG_LBUTTON): # 按住左键拖曳，画框

  print("2-EVENT_FLAG_LBUTTON")

  cv2.rectangle(img2, point1, (x, y), (255, 0, 0), thickness=2)

  cv2.imshow('image', img2)

 

 elif event == cv2.EVENT_LBUTTONUP: # 左键释放，显示

  print("3-EVENT_LBUTTONUP")

  point2 = (x, y)

  cv2.rectangle(img2, point1, point2, (0, 0, 255), thickness=2)

  cv2.imshow('image', img2)

  if point1!=point2:

   min_x = min(point1[0], point2[0])

   min_y = min(point1[1], point2[1])

   width = abs(point1[0] - point2[0])

   height = abs(point1[1] - point2[1])

   g_rect=[min_x,min_y,width,height]

   cut_img = img[min_y:min_y + height, min_x:min_x + width]

   cv2.imshow('ROI', cut_img)

 

def get_image_roi(rgb_image):

 '''

 获得用户ROI区域的rect=[x,y,w,h]

 :param rgb_image:

 :return:

 '''

 bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

 global img

 img=bgr_image

 cv2.namedWindow('image')

 while True:

  cv2.setMouseCallback('image', on_mouse)

  # cv2.startWindowThread() # 加在这个位置

  cv2.imshow('image', img)

  key=cv2.waitKey(0)

  if key==13 or key==32:#按空格和回车键退出

   break

 cv2.destroyAllWindows()

 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

 return g_rect

 

def select_user_roi(image_path):

 '''

 由于原图的分辨率较大，这里缩小后获取ROI，返回时需要重新scale对应原图

 :param image_path:

 :return:

 '''

 orig_image = image_processing.read_image(image_path)

 orig_shape = np.shape(orig_image)

 resize_image = image_processing.resize_image(orig_image, resize_height=800,resize_width=None)

 re_shape = np.shape(resize_image)

 g_rect=get_image_roi(resize_image)

 orgi_rect = image_processing.scale_rect(g_rect, re_shape,orig_shape)

 roi_image=image_processing.get_rect_image(orig_image,orgi_rect)

 image_processing.cv_show_image("RECT",roi_image)

 image_processing.show_image_rect("image",orig_image,orgi_rect)

 return orgi_rect

 

 

if __name__ == '__main__':

 # image_path="../dataset/images/IMG_0007.JPG"

 image_path="../dataset/test_images/lena.jpg"

 

 # rect=get_image_roi(image)

 rect=select_user_roi(image_path)

 print(rect)



其中image_processing.py文件如下：





# -*-coding: utf-8 -*-

"""

 @Project: IntelligentManufacture

 @File : image_processing.py

 @Author : panjq

 @E-mail : pan_jinquan@163.com

 @Date : 2019-02-14 15:34:50

"""

 

import os

import glob

import cv2

import numpy as np

import matplotlib.pyplot as plt

 

def show_image(title, image):

 '''

 调用matplotlib显示RGB图片

 :param title: 图像标题

 :param image: 图像的数据

 :return:

 '''

 # plt.figure("show_image")

 # print(image.dtype)

 plt.imshow(image)

 plt.axis('on') # 关掉坐标轴为 off

 plt.title(title) # 图像题目

 plt.show()

 

def cv_show_image(title, image):

 '''

 调用OpenCV显示RGB图片

 :param title: 图像标题

 :param image: 输入RGB图像

 :return:

 '''

 channels=image.shape[-1]

 if channels==3:

  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # 将BGR转为RGB

 cv2.imshow(title,image)

 cv2.waitKey(0)

 

def read_image(filename, resize_height=None, resize_width=None, normalization=False):

 '''

 读取图片数据,默认返回的是uint8,[0,255]

 :param filename:

 :param resize_height:

 :param resize_width:

 :param normalization:是否归一化到[0.,1.0]

 :return: 返回的RGB图片数据

 '''

 

 bgr_image = cv2.imread(filename)

 # bgr_image = cv2.imread(filename,cv2.IMREAD_IGNORE_ORIENTATION|cv2.IMREAD_COLOR)

 if bgr_image is None:

  print("Warning:不存在:{}", filename)

  return None

 if len(bgr_image.shape) == 2: # 若是灰度图则转为三通道

  print("Warning:gray image", filename)

  bgr_image = cv2.cvtColor(bgr_image, cv2.COLOR_GRAY2BGR)

 

 rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB) # 将BGR转为RGB

 # show_image(filename,rgb_image)

 # rgb_image=Image.open(filename)

 rgb_image = resize_image(rgb_image,resize_height,resize_width)

 rgb_image = np.asanyarray(rgb_image)

 if normalization:

  # 不能写成:rgb_image=rgb_image/255

  rgb_image = rgb_image / 255.0

 # show_image("src resize image",image)

 return rgb_image

def resize_image(image,resize_height, resize_width):

 '''

 :param image:

 :param resize_height:

 :param resize_width:

 :return:

 '''

 image_shape=np.shape(image)

 height=image_shape[0]

 width=image_shape[1]

 if (resize_height is None) and (resize_width is None):#错误写法：resize_height and resize_width is None

  return image

 if resize_height is None:

  resize_height=int(height*resize_width/width)

 elif resize_width is None:

  resize_width=int(width*resize_height/height)

 image = cv2.resize(image, dsize=(resize_width, resize_height))

 return image

def scale_image(image,scale):

 '''

 :param image:

 :param scale: (scale_w,scale_h)

 :return:

 '''

 image = cv2.resize(image,dsize=None, fx=scale[0],fy=scale[1])

 return image

 

 

def get_rect_image(image,rect):

 '''

 :param image:

 :param rect: [x,y,w,h]

 :return:

 '''

 x, y, w, h=rect

 cut_img = image[y:(y+ h),x:(x+w)]

 return cut_img

def scale_rect(orig_rect,orig_shape,dest_shape):

 '''

 对图像进行缩放时，对应的rectangle也要进行缩放

 :param orig_rect: 原始图像的rect=[x,y,w,h]

 :param orig_shape: 原始图像的维度shape=[h,w]

 :param dest_shape: 缩放后图像的维度shape=[h,w]

 :return: 经过缩放后的rectangle

 '''

 new_x=int(orig_rect[0]*dest_shape[1]/orig_shape[1])

 new_y=int(orig_rect[1]*dest_shape[0]/orig_shape[0])

 new_w=int(orig_rect[2]*dest_shape[1]/orig_shape[1])

 new_h=int(orig_rect[3]*dest_shape[0]/orig_shape[0])

 dest_rect=[new_x,new_y,new_w,new_h]

 return dest_rect

 

def show_image_rect(win_name,image,rect):

 '''

 :param win_name:

 :param image:

 :param rect:

 :return:

 '''

 x, y, w, h=rect

 point1=(x,y)

 point2=(x+w,y+h)

 cv2.rectangle(image, point1, point2, (0, 0, 255), thickness=2)

 cv_show_image(win_name, image)

 

def rgb_to_gray(image):

 image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

 return image

 

def save_image(image_path, rgb_image,toUINT8=True):

 if toUINT8:

  rgb_image = np.asanyarray(rgb_image * 255, dtype=np.uint8)

 if len(rgb_image.shape) == 2: # 若是灰度图则转为三通道

  bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_GRAY2BGR)

 else:

  bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

 cv2.imwrite(image_path, bgr_image)

 

def combime_save_image(orig_image, dest_image, out_dir,name,prefix):

 '''

 命名标准：out_dir/name_prefix.jpg

 :param orig_image:

 :param dest_image:

 :param image_path:

 :param out_dir:

 :param prefix:

 :return:

 '''

 dest_path = os.path.join(out_dir, name + "_"+prefix+".jpg")

 save_image(dest_path, dest_image)

 

 dest_image = np.hstack((orig_image, dest_image))

 save_image(os.path.join(out_dir, "{}_src_{}.jpg".format(name,prefix)), dest_image)

 

if __name__=="__main__":

 image_path="../dataset/test_images/src.jpg"

 image = read_image(image_path, resize_height=None, resize_width=None)

 image = rgb_to_gray(image)

 orig_shape=np.shape(image)#shape=(h,w)

 orig_rect=[50,100,100,200]#x,y,w,h

 print("orig_shape:{}".format(orig_shape))

 show_image_rect("orig",image,orig_rect)

 

 dest_image=resize_image(image,resize_height=None,resize_width=200)

 dest_shape=np.shape(dest_image)

 print("dest_shape:{}".format(dest_shape))

 dest_rect=scale_rect(orig_rect, orig_shape, dest_shape)

 show_image_rect("dest",dest_image,dest_rect)



以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持中文源码网。